# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""A set of functions that are used for visualization.

These functions often receive an image, perform some visualization on the image.
The functions do not return a value, instead they modify the image itself.

"""
import collections
import functools
# Set headless-friendly backend.
import matplotlib

matplotlib.use('Agg')  # pylint: disable=multiple-statements
import matplotlib.pyplot as plt  # pylint: disable=g-import-not-at-top
import numpy as np
import PIL.Image as Image
import PIL.ImageColor as ImageColor
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as ImageFont
import six
import tensorflow as tf

from tf_server import standard_fields as fields

_TITLE_LEFT_MARGIN = 10
_TITLE_TOP_MARGIN = 10
STANDARD_COLORS = [
    'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
    'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
    'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan',
    'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
    'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
    'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite',
    'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
    'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki',
    'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
    'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
    'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
    'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
    'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
    'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
    'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
    'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed',
    'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
    'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
    'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
    'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
    'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
    'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
    'WhiteSmoke', 'Yellow', 'YellowGreen'
]


def save_image_array_as_png(image, output_path):
    """Saves an image (represented as a numpy array) to PNG.

    Args:
      image: a numpy array with shape [height, width, 3].
      output_path: path to which image should be written.
    """
    image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
    with tf.gfile.Open(output_path, 'w') as fid:
        image_pil.save(fid, 'PNG')


def encode_image_array_as_png_str(image):
    """Encodes a numpy array into a PNG string.

    Args:
      image: a numpy array with shape [height, width, 3].

    Returns:
      PNG encoded image string.
    """
    image_pil = Image.fromarray(np.uint8(image))
    output = six.BytesIO()
    image_pil.save(output, format='PNG')
    png_string = output.getvalue()
    output.close()
    return png_string


def draw_bounding_box_on_image_array(image,
                                     ymin,
                                     xmin,
                                     ymax,
                                     xmax,
                                     color='red',
                                     thickness=4,
                                     display_str_list=(),
                                     use_normalized_coordinates=True):
    """Adds a bounding box to an image (numpy array).

    Bounding box coordinates can be specified in either absolute (pixel) or
    normalized coordinates by setting the use_normalized_coordinates argument.

    Args:
      image: a numpy array with shape [height, width, 3].
      ymin: ymin of bounding box.
      xmin: xmin of bounding box.
      ymax: ymax of bounding box.
      xmax: xmax of bounding box.
      color: color to draw bounding box. Default is red.
      thickness: line thickness. Default value is 4.
      display_str_list: list of strings to display in box
                        (each to be shown on its own line).
      use_normalized_coordinates: If True (default), treat coordinates
        ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
        coordinates as absolute.
    """
    image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
    draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
                               thickness, display_str_list,
                               use_normalized_coordinates)
    np.copyto(image, np.array(image_pil))


def draw_bounding_box_on_image(image,
                               ymin,
                               xmin,
                               ymax,
                               xmax,
                               color='red',
                               thickness=4,
                               display_str_list=(),
                               use_normalized_coordinates=True):
    """Adds a bounding box to an image.

    Bounding box coordinates can be specified in either absolute (pixel) or
    normalized coordinates by setting the use_normalized_coordinates argument.

    Each string in display_str_list is displayed on a separate line above the
    bounding box in black text on a rectangle filled with the input 'color'.
    If the top of the bounding box extends to the edge of the image, the strings
    are displayed below the bounding box.

    Args:
      image: a PIL.Image object.
      ymin: ymin of bounding box.
      xmin: xmin of bounding box.
      ymax: ymax of bounding box.
      xmax: xmax of bounding box.
      color: color to draw bounding box. Default is red.
      thickness: line thickness. Default value is 4.
      display_str_list: list of strings to display in box
                        (each to be shown on its own line).
      use_normalized_coordinates: If True (default), treat coordinates
        ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
        coordinates as absolute.
    """
    draw = ImageDraw.Draw(image)
    im_width, im_height = image.size
    if use_normalized_coordinates:
        (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                      ymin * im_height, ymax * im_height)
    else:
        (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
    draw.line([(left, top), (left, bottom), (right, bottom),
               (right, top), (left, top)], width=thickness, fill=color)
    try:
        font = ImageFont.truetype('arial.ttf', 24)
    except IOError:
        font = ImageFont.load_default()

    # If the total height of the display strings added to the top of the bounding
    # box exceeds the top of the image, stack the strings below the bounding box
    # instead of above.
    display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
    # Each display_str has a top and bottom margin of 0.05x.
    total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)

    if top > total_display_str_height:
        text_bottom = top
    else:
        text_bottom = bottom + total_display_str_height
    # Reverse list and print from bottom to top.
    for display_str in display_str_list[::-1]:
        text_width, text_height = font.getsize(display_str)
        margin = np.ceil(0.05 * text_height)
        draw.rectangle(
            [(left, text_bottom - text_height - 2 * margin), (left + text_width,
                                                              text_bottom)],
            fill=color)
        draw.text(
            (left + margin, text_bottom - text_height - margin),
            display_str,
            fill='black',
            font=font)
        text_bottom -= text_height - 2 * margin


def draw_bounding_boxes_on_image_array(image,
                                       boxes,
                                       color='red',
                                       thickness=4,
                                       display_str_list_list=()):
    """Draws bounding boxes on image (numpy array).

    Args:
      image: a numpy array object.
      boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
             The coordinates are in normalized format between [0, 1].
      color: color to draw bounding box. Default is red.
      thickness: line thickness. Default value is 4.
      display_str_list_list: list of list of strings.
                             a list of strings for each bounding box.
                             The reason to pass a list of strings for a
                             bounding box is that it might contain
                             multiple labels.

    Raises:
      ValueError: if boxes is not a [N, 4] array
    """
    image_pil = Image.fromarray(image)
    draw_bounding_boxes_on_image(image_pil, boxes, color, thickness,
                                 display_str_list_list)
    np.copyto(image, np.array(image_pil))


def draw_bounding_boxes_on_image(image,
                                 boxes,
                                 color='red',
                                 thickness=4,
                                 display_str_list_list=()):
    """Draws bounding boxes on image.

    Args:
      image: a PIL.Image object.
      boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
             The coordinates are in normalized format between [0, 1].
      color: color to draw bounding box. Default is red.
      thickness: line thickness. Default value is 4.
      display_str_list_list: list of list of strings.
                             a list of strings for each bounding box.
                             The reason to pass a list of strings for a
                             bounding box is that it might contain
                             multiple labels.

    Raises:
      ValueError: if boxes is not a [N, 4] array
    """
    boxes_shape = boxes.shape
    if not boxes_shape:
        return
    if len(boxes_shape) != 2 or boxes_shape[1] != 4:
        raise ValueError('Input must be of size [N, 4]')
    for i in range(boxes_shape[0]):
        display_str_list = ()
        if display_str_list_list:
            display_str_list = display_str_list_list[i]
        draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2],
                                   boxes[i, 3], color, thickness, display_str_list)


def _visualize_boxes(image, boxes, classes, scores, category_index, **kwargs):
    return visualize_boxes_and_labels_on_image_array(
        image, boxes, classes, scores, category_index=category_index, **kwargs)


def _visualize_boxes_and_masks(image, boxes, classes, scores, masks,
                               category_index, **kwargs):
    return visualize_boxes_and_labels_on_image_array(
        image,
        boxes,
        classes,
        scores,
        category_index=category_index,
        instance_masks=masks,
        **kwargs)


def _visualize_boxes_and_keypoints(image, boxes, classes, scores, keypoints,
                                   category_index, **kwargs):
    return visualize_boxes_and_labels_on_image_array(
        image,
        boxes,
        classes,
        scores,
        category_index=category_index,
        keypoints=keypoints,
        **kwargs)


def _visualize_boxes_and_masks_and_keypoints(
        image, boxes, classes, scores, masks, keypoints, category_index, **kwargs):
    return visualize_boxes_and_labels_on_image_array(
        image,
        boxes,
        classes,
        scores,
        category_index=category_index,
        instance_masks=masks,
        keypoints=keypoints,
        **kwargs)


def draw_bounding_boxes_on_image_tensors(images,
                                         boxes,
                                         classes,
                                         scores,
                                         category_index,
                                         instance_masks=None,
                                         keypoints=None,
                                         max_boxes_to_draw=20,
                                         min_score_thresh=0.2):
    """Draws bounding boxes, masks, and keypoints on batch of image tensors.

    Args:
      images: A 4D uint8 image tensor of shape [N, H, W, C].
      boxes: [N, max_detections, 4] float32 tensor of detection boxes.
      classes: [N, max_detections] int tensor of detection classes. Note that
        classes are 1-indexed.
      scores: [N, max_detections] float32 tensor of detection scores.
      category_index: a dict that maps integer ids to category dicts. e.g.
        {1: {1: 'dog'}, 2: {2: 'cat'}, ...}
      instance_masks: A 4D uint8 tensor of shape [N, max_detection, H, W] with
        instance masks.
      keypoints: A 4D float32 tensor of shape [N, max_detection, num_keypoints, 2]
        with keypoints.
      max_boxes_to_draw: Maximum number of boxes to draw on an image. Default 20.
      min_score_thresh: Minimum score threshold for visualization. Default 0.2.

    Returns:
      4D image tensor of type uint8, with boxes drawn on top.
    """
    visualization_keyword_args = {
        'use_normalized_coordinates': True,
        'max_boxes_to_draw': max_boxes_to_draw,
        'min_score_thresh': min_score_thresh,
        'agnostic_mode': False,
        'line_thickness': 4
    }

    if instance_masks is not None and keypoints is None:
        visualize_boxes_fn = functools.partial(
            _visualize_boxes_and_masks,
            category_index=category_index,
            **visualization_keyword_args)
        elems = [images, boxes, classes, scores, instance_masks]
    elif instance_masks is None and keypoints is not None:
        visualize_boxes_fn = functools.partial(
            _visualize_boxes_and_keypoints,
            category_index=category_index,
            **visualization_keyword_args)
        elems = [images, boxes, classes, scores, keypoints]
    elif instance_masks is not None and keypoints is not None:
        visualize_boxes_fn = functools.partial(
            _visualize_boxes_and_masks_and_keypoints,
            category_index=category_index,
            **visualization_keyword_args)
        elems = [images, boxes, classes, scores, instance_masks, keypoints]
    else:
        visualize_boxes_fn = functools.partial(
            _visualize_boxes,
            category_index=category_index,
            **visualization_keyword_args)
        elems = [images, boxes, classes, scores]

    def draw_boxes(image_and_detections):
        """Draws boxes on image."""
        image_with_boxes = tf.py_func(visualize_boxes_fn, image_and_detections,
                                      tf.uint8)
        return image_with_boxes

    images = tf.map_fn(draw_boxes, elems, dtype=tf.uint8, back_prop=False)
    return images


def draw_side_by_side_evaluation_image(eval_dict,
                                       category_index,
                                       max_boxes_to_draw=20,
                                       min_score_thresh=0.2):
    """Creates a side-by-side image with detections and groundtruth.

    Bounding boxes (and instance masks, if available) are visualized on both
    subimages.

    Args:
      eval_dict: The evaluation dictionary returned by
        eval_util.result_dict_for_single_example().
      category_index: A category index (dictionary) produced from a labelmap.
      max_boxes_to_draw: The maximum number of boxes to draw for detections.
      min_score_thresh: The minimum score threshold for showing detections.

    Returns:
      A [1, H, 2 * W, C] uint8 tensor. The subimage on the left corresponds to
        detections, while the subimage on the right corresponds to groundtruth.
    """
    detection_fields = fields.DetectionResultFields()
    input_data_fields = fields.InputDataFields()
    instance_masks = None
    if detection_fields.detection_masks in eval_dict:
        instance_masks = tf.cast(
            tf.expand_dims(eval_dict[detection_fields.detection_masks], axis=0),
            tf.uint8)
    keypoints = None
    if detection_fields.detection_keypoints in eval_dict:
        keypoints = tf.expand_dims(
            eval_dict[detection_fields.detection_keypoints], axis=0)
    groundtruth_instance_masks = None
    if input_data_fields.groundtruth_instance_masks in eval_dict:
        groundtruth_instance_masks = tf.cast(
            tf.expand_dims(
                eval_dict[input_data_fields.groundtruth_instance_masks], axis=0),
            tf.uint8)
    images_with_detections = draw_bounding_boxes_on_image_tensors(
        eval_dict[input_data_fields.original_image],
        tf.expand_dims(eval_dict[detection_fields.detection_boxes], axis=0),
        tf.expand_dims(eval_dict[detection_fields.detection_classes], axis=0),
        tf.expand_dims(eval_dict[detection_fields.detection_scores], axis=0),
        category_index,
        instance_masks=instance_masks,
        keypoints=keypoints,
        max_boxes_to_draw=max_boxes_to_draw,
        min_score_thresh=min_score_thresh)
    images_with_groundtruth = draw_bounding_boxes_on_image_tensors(
        eval_dict[input_data_fields.original_image],
        tf.expand_dims(eval_dict[input_data_fields.groundtruth_boxes], axis=0),
        tf.expand_dims(eval_dict[input_data_fields.groundtruth_classes], axis=0),
        tf.expand_dims(
            tf.ones_like(
                eval_dict[input_data_fields.groundtruth_classes],
                dtype=tf.float32),
            axis=0),
        category_index,
        instance_masks=groundtruth_instance_masks,
        keypoints=None,
        max_boxes_to_draw=None,
        min_score_thresh=0.0)
    return tf.concat([images_with_detections, images_with_groundtruth], axis=2)


def draw_keypoints_on_image_array(image,
                                  keypoints,
                                  color='red',
                                  radius=2,
                                  use_normalized_coordinates=True):
    """Draws keypoints on an image (numpy array).

    Args:
      image: a numpy array with shape [height, width, 3].
      keypoints: a numpy array with shape [num_keypoints, 2].
      color: color to draw the keypoints with. Default is red.
      radius: keypoint radius. Default value is 2.
      use_normalized_coordinates: if True (default), treat keypoint values as
        relative to the image.  Otherwise treat them as absolute.
    """
    image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
    draw_keypoints_on_image(image_pil, keypoints, color, radius,
                            use_normalized_coordinates)
    np.copyto(image, np.array(image_pil))


def draw_keypoints_on_image(image,
                            keypoints,
                            color='red',
                            radius=2,
                            use_normalized_coordinates=True):
    """Draws keypoints on an image.

    Args:
      image: a PIL.Image object.
      keypoints: a numpy array with shape [num_keypoints, 2].
      color: color to draw the keypoints with. Default is red.
      radius: keypoint radius. Default value is 2.
      use_normalized_coordinates: if True (default), treat keypoint values as
        relative to the image.  Otherwise treat them as absolute.
    """
    draw = ImageDraw.Draw(image)
    im_width, im_height = image.size
    keypoints_x = [k[1] for k in keypoints]
    keypoints_y = [k[0] for k in keypoints]
    if use_normalized_coordinates:
        keypoints_x = tuple([im_width * x for x in keypoints_x])
        keypoints_y = tuple([im_height * y for y in keypoints_y])
    for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y):
        draw.ellipse([(keypoint_x - radius, keypoint_y - radius),
                      (keypoint_x + radius, keypoint_y + radius)],
                     outline=color, fill=color)


def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
    """Draws mask on an image.

    Args:
      image: uint8 numpy array with shape (img_height, img_height, 3)
      mask: a uint8 numpy array of shape (img_height, img_height) with
        values between either 0 or 1.
      color: color to draw the keypoints with. Default is red.
      alpha: transparency value between 0 and 1. (default: 0.4)

    Raises:
      ValueError: On incorrect data type for image or masks.
    """
    if image.dtype != np.uint8:
        raise ValueError('`image` not of type np.uint8')
    if mask.dtype != np.uint8:
        raise ValueError('`mask` not of type np.uint8')
    if np.any(np.logical_and(mask != 1, mask != 0)):
        raise ValueError('`mask` elements should be in [0, 1]')
    if image.shape[:2] != mask.shape:
        raise ValueError('The image has spatial dimensions %s but the mask has '
                         'dimensions %s' % (image.shape[:2], mask.shape))
    rgb = ImageColor.getrgb(color)
    pil_image = Image.fromarray(image)

    solid_color = np.expand_dims(
        np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
    pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
    pil_mask = Image.fromarray(np.uint8(255.0 * alpha * mask)).convert('L')
    pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
    np.copyto(image, np.array(pil_image.convert('RGB')))


def visualize_boxes_and_labels_on_image_array(
        image,
        boxes,
        classes,
        scores,
        category_index,
        instance_masks=None,
        instance_boundaries=None,
        keypoints=None,
        use_normalized_coordinates=False,
        max_boxes_to_draw=20,
        min_score_thresh=.5,
        agnostic_mode=False,
        line_thickness=4,
        groundtruth_box_visualization_color='black',
        skip_scores=False,
        skip_labels=False):
    """Overlay labeled boxes on an image with formatted scores and label names.

    This function groups boxes that correspond to the same location
    and creates a display string for each detection and overlays these
    on the image. Note that this function modifies the image in place, and returns
    that same image.

    Args:
      image: uint8 numpy array with shape (img_height, img_width, 3)
      boxes: a numpy array of shape [N, 4]
      classes: a numpy array of shape [N]. Note that class indices are 1-based,
        and match the keys in the label map.
      scores: a numpy array of shape [N] or None.  If scores=None, then
        this function assumes that the boxes to be plotted are groundtruth
        boxes and plot all boxes as black with no classes or scores.
      category_index: a dict containing category dictionaries (each holding
        category index `id` and category name `name`) keyed by category indices.
      instance_masks: a numpy array of shape [N, image_height, image_width] with
        values ranging between 0 and 1, can be None.
      instance_boundaries: a numpy array of shape [N, image_height, image_width]
        with values ranging between 0 and 1, can be None.
      keypoints: a numpy array of shape [N, num_keypoints, 2], can
        be None
      use_normalized_coordinates: whether boxes is to be interpreted as
        normalized coordinates or not.
      max_boxes_to_draw: maximum number of boxes to visualize.  If None, draw
        all boxes.
      min_score_thresh: minimum score threshold for a box to be visualized
      agnostic_mode: boolean (default: False) controlling whether to evaluate in
        class-agnostic mode or not.  This mode will display scores but ignore
        classes.
      line_thickness: integer (default: 4) controlling line width of the boxes.
      groundtruth_box_visualization_color: box color for visualizing groundtruth
        boxes
      skip_scores: whether to skip score when drawing a single detection
      skip_labels: whether to skip label when drawing a single detection

    Returns:
      uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes.
    """
    # Create a display string (and color) for every box location, group any boxes
    # that correspond to the same location.
    box_to_display_str_map = collections.defaultdict(list)
    box_to_color_map = collections.defaultdict(str)
    box_to_instance_masks_map = {}
    box_to_instance_boundaries_map = {}
    box_to_keypoints_map = collections.defaultdict(list)
    if not max_boxes_to_draw:
        max_boxes_to_draw = boxes.shape[0]
    for i in range(min(max_boxes_to_draw, boxes.shape[0])):
        if scores is None or scores[i] > min_score_thresh:
            box = tuple(boxes[i].tolist())
            if instance_masks is not None:
                box_to_instance_masks_map[box] = instance_masks[i]
            if instance_boundaries is not None:
                box_to_instance_boundaries_map[box] = instance_boundaries[i]
            if keypoints is not None:
                box_to_keypoints_map[box].extend(keypoints[i])
            if scores is None:
                box_to_color_map[box] = groundtruth_box_visualization_color
            else:
                display_str = ''
                if not skip_labels:
                    if not agnostic_mode:
                        if classes[i] in category_index.keys():
                            class_name = category_index[classes[i]]['name']
                        else:
                            class_name = 'N/A'
                        display_str = str(class_name)
                if not skip_scores:
                    if not display_str:
                        display_str = '{}%'.format(int(100 * scores[i]))
                    else:
                        display_str = '{}: {}%'.format(display_str, int(100 * scores[i]))
                box_to_display_str_map[box].append(display_str)
                if agnostic_mode:
                    box_to_color_map[box] = 'DarkOrange'
                else:
                    box_to_color_map[box] = STANDARD_COLORS[
                        classes[i] % len(STANDARD_COLORS)]

    # Draw all boxes onto image.
    for box, color in box_to_color_map.items():
        ymin, xmin, ymax, xmax = box
        if instance_masks is not None:
            draw_mask_on_image_array(
                image,
                box_to_instance_masks_map[box],
                color=color
            )
        if instance_boundaries is not None:
            draw_mask_on_image_array(
                image,
                box_to_instance_boundaries_map[box],
                color='red',
                alpha=1.0
            )
        draw_bounding_box_on_image_array(
            image,
            ymin,
            xmin,
            ymax,
            xmax,
            color=color,
            thickness=line_thickness,
            display_str_list=box_to_display_str_map[box],
            use_normalized_coordinates=use_normalized_coordinates)
        if keypoints is not None:
            draw_keypoints_on_image_array(
                image,
                box_to_keypoints_map[box],
                color=color,
                radius=line_thickness / 2,
                use_normalized_coordinates=use_normalized_coordinates)

    return image


def add_cdf_image_summary(values, name):
    """Adds a tf.summary.image for a CDF plot of the values.

    Normalizes `values` such that they sum to 1, plots the cumulative distribution
    function and creates a tf image summary.

    Args:
      values: a 1-D float32 tensor containing the values.
      name: name for the image summary.
    """

    def cdf_plot(values):
        """Numpy function to plot CDF."""
        normalized_values = values / np.sum(values)
        sorted_values = np.sort(normalized_values)
        cumulative_values = np.cumsum(sorted_values)
        fraction_of_examples = (np.arange(cumulative_values.size, dtype=np.float32)
                                / cumulative_values.size)
        fig = plt.figure(frameon=False)
        ax = fig.add_subplot('111')
        ax.plot(fraction_of_examples, cumulative_values)
        ax.set_ylabel('cumulative normalized values')
        ax.set_xlabel('fraction of examples')
        fig.canvas.draw()
        width, height = fig.get_size_inches() * fig.get_dpi()
        image = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
            1, int(height), int(width), 3)
        return image

    cdf_plot = tf.py_func(cdf_plot, [values], tf.uint8)
    tf.summary.image(name, cdf_plot)
